AIF-C01 Guidelines for Responsible AI Practice Question
A machine learning team is building a credit risk model and discovers that the training data has a significant imbalance in loan approval rates between two demographic groups. They decide to reweight the training samples using a preprocessing technique. Which SageMaker Clarify feature can help compute the appropriate sample weights to achieve demographic parity?
⚠ Common exam trap
It's easy for candidates to confuse post-training bias metrics (Option B) with pre-training mitigation techniques, assuming that measuring bias is the same as correcting it via sample weights.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Clarify preprocessing (reweighting)
SageMaker Clarify's preprocessing (reweighting) feature directly computes sample weights to adjust for imbalances in training data, enabling demographic parity by assigning higher weights to underrepresented groups. This is a pre-training bias mitigation technique that modifies the dataset before model training, aligning with the team's goal of reweighting samples to address loan approval rate disparities.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Clarify preprocessing (reweighting)
Why this is correct
Clarify provides a preprocessing transformation that reweights data to meet fairness constraints.
- ✗
Clarify post-training bias metrics
Why it's wrong here
Post-training metrics measure bias after training, not reweighting before.
- ✗
Model Monitor bias drift
Why it's wrong here
Model Monitor detects drift post-deployment, not preprocessing.
- ✗
Clarify explainability (SHAP)
Why it's wrong here
SHAP explains predictions, not reweights data.
Quick reference
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.